scholarly journals The Use of a Vortex Insertion Technique to Simulate the Extratropical Transition of Hurricane Michael (2000)

2007 ◽  
Vol 22 (3) ◽  
pp. 480-500 ◽  
Author(s):  
Christopher T. Fogarty ◽  
Richard J. Greatbatch ◽  
Harold Ritchie
Keyword(s):  
Nova Scotia ◽  
North Atlantic Ocean ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Transition Process ◽  
Limited Area ◽  
Model Parameters ◽  
Extratropical Transition ◽  
Wind Speeds ◽  
Synthetic Vortex

Abstract On 19 October 2000, Hurricane Michael merged with an approaching baroclinic trough over the western North Atlantic Ocean south of Nova Scotia. As the hurricane moved over cooler sea surface temperatures (SSTs; less than 25°C), it intensified to category-2 intensity on the Saffir–Simpson hurricane scale [maximum sustained wind speeds of 44 m s−1 (85 kt)] while tapping energy from the baroclinic environment. The large “hybrid” storm made landfall on the south coast of Newfoundland with maximum sustained winds of 39 m s−1 (75 kt) causing moderate damage to coastal communities east of landfall. Hurricane Michael presented significant challenges to weather forecasters. The fundamental issue was determining which of two cyclones (a newly formed baroclinic low south of Nova Scotia or the hurricane) would become the dominant circulation center during the early stages of the extratropical transition (ET) process. Second, it was difficult to predict the intensity of the storm at landfall owing to competing factors: 1) decreasing SSTs conducive to weakening and 2) the approaching negatively tilted upper-level trough, favoring intensification. Numerical hindcast simulations using the limited-area Mesoscale Compressible Community model with synthetic vortex insertion (cyclone bogus) prior to the ET of Hurricane Michael led to a more realistic evolution of wind and pressure compared to running the model without vortex insertion. Specifically, the mesoscale model correctly simulates the hurricane as the dominant circulation center early in the transition process, versus the baroclinic low to its north, which was the favored development in the runs not employing vortex insertion. A suite of experiments is conducted to establish the sensitivity of the ET to various initial conditions, lateral driving fields, domain sizes, and model parameters. The resulting storm tracks and intensities fall within the range of the operational guidance, lending support to the possibility of improving numerical forecasts using synthetic vortex insertion prior to ET in such a model.

scholarly journals The Impact of Variational Assimilation of SSM/I and QuikSCAT Satellite Observations on the Numerical Simulation of Indian Ocean Tropical Cyclones

2008 ◽  
Vol 23 (3) ◽  
pp. 460-476 ◽  
Author(s):  
Randhir Singh ◽  
P. K. Pal ◽  
C. M. Kishtawal ◽  
P. C. Joshi
Keyword(s):  
Indian Ocean ◽  
Bay Of Bengal ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Lower Troposphere ◽  
Heat Fluxes ◽  
Cyclone Intensity ◽  
Wind Speeds ◽  
The Impact ◽  
Quikscat Winds

Abstract In this study, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with three-dimensional variational data assimilation (3DVAR) is utilized to investigate the influence of Special Sensor Microwave Imager (SSM/I) and Quick Scatterometer (QuikSCAT) observations on the prediction of an Indian Ocean tropical cyclone. The 3DVAR sensitivity runs were conducted separately with QuikSCAT wind vectors, SSM/I wind speeds, and total precipitable water (TPW) to investigate their individual impact on cyclone intensity and track. The Orissa supercyclone over the Bay of Bengal during October 1999 was used for simulation and assimilation experiments. Assimilation of the QuikSCAT wind vector improves the initial position of the cyclone’s center with a position error of 33 km, which was 163 km in the background analysis. Incorporation of QuikSCAT winds was found to increase the air–sea heat fluxes over the cyclonic region, which resulted in the improved simulated intensity when compared with the simulation made without QuikSCAT winds in the initial conditions. The cyclone track improved significantly with assimilation of QuikSCAT wind vectors. The track improvement resulted from relocation of the initial cyclonic vortex after assimilation of QuikSCAT wind vectors. Like QuikSCAT, assimilation of SSM/I wind speeds strengthened the cyclonic circulation in the initial conditions. This increase in the low-level wind speeds enhanced the air–sea exchange processes and improved the simulated intensity of the cyclone. The lack of information about the wind direction from SSM/I prevented it from making much of an impact on track prediction. As compared to the first guess, assimilation of the SSM/I TPW shows a moistening of the lower troposphere over most of the Bay of Bengal except over the central region of the cyclone, where the assimilation of SSM/I TPW reduces the lower-tropospheric moisture. This decrease of moisture in the TPW assimilation experiment resulted in a weak cyclone intensity.

scholarly journals Initial-Condition Sensitivities and the Predictability of Downslope Winds

2009 ◽  
Vol 66 (11) ◽  
pp. 3401-3418 ◽  
Author(s):  
Patrick A. Reinecke ◽  
Dale R. Durran
Keyword(s):  
Wave Breaking ◽  
Initial Conditions ◽  
Static Stability ◽  
Synoptic Scale ◽  
Wind Speeds ◽  
Downslope Winds ◽  
First Case ◽  
Downslope Wind ◽  
The Difference ◽  
Strong Winds

Abstract The sensitivity of downslope wind forecasts to small changes in initial conditions is explored by using 70-member ensemble simulations of two prototypical windstorms observed during the Terrain-Induced Rotor Experiment (T-REX). The 10 weakest and 10 strongest ensemble members are composited and compared for each event. In the first case, the 6-h ensemble-mean forecast shows a large-amplitude breaking mountain wave and severe downslope winds. Nevertheless, the forecasts are very sensitive to the initial conditions because the difference in the downslope wind speeds predicted by the strong- and weak-member composites grows to larger than 28 m s−1 over the 6-h forecast. The structure of the synoptic-scale flow one hour prior to the windstorm and during the windstorm is very similar in both the weak- and strong-member composites. Wave breaking is not a significant factor in the second case, in which the strong winds are generated by a layer of high static stability flowing beneath a layer of weaker mid- and upper-tropospheric stability. In this case, the sensitivity to initial conditions is weaker but still significant. The difference in downslope wind speeds between the weak- and strong-member composites grows to 22 m s−1 over 12 h. During and one hour before the windstorm, the synoptic-scale flow exhibits appreciable differences between the strong- and weak-member composites. Although this case appears to be more predictable than the wave-breaking event, neither case suggests that much confidence should be placed in the intensity of downslope winds forecast 12 or more hours in advance.

Kinetic Model of Adiabatic Temperature Rise of Mass Concrete

2021 ◽  
Vol 13 (5) ◽  
pp. 771-780
Author(s):  
Shou-Kai Chen ◽  
Bo-Wen Xu
Keyword(s):  
Temperature Field ◽  
Temperature Rise ◽  
Initial Conditions ◽  
Dynamic Change ◽  
Adiabatic Temperature ◽  
Model Parameters ◽  
Hydration Reaction ◽  
Mass Concrete ◽  
Adiabatic Temperature Rise ◽  
Proposed Model

The adiabatic temperature rise model of mass concrete is very important for temperature field simulation, same to crack resistance capacity and temperature control of concrete structures. In this research, a thermal kinetics analysis was performed to study the exothermic hydration reaction process of concrete, and an adiabatic temperature rise model was proposed. The proposed model considers influencing factors, including initial temperature, temperature history, activation energy, and the completion degree of adiabatic temperature rise and is theoretically mature and definitive in physical meaning. It was performed on different initial temperatures for adiabatic temperature rise test; the data were employed in a regression analysis of the model parameters and initial conditions. The same function was applied to describe the dynamic change of the adiabatic temperature rise rates for different initial temperatures and different temperature changing processes and subsequently employed in a finite element analysis of the concrete temperature field. The test results indicated that the proposed model adequately fits the data of the adiabatic temperature rise test, which included different initial temperatures, and accurately predicts the changing pattern of adiabatic temperature rise of concrete at different initial temperatures. Compared with the results using the traditional age-based adiabatic temperature rise model, the results of a calculation example revealed that the simulated calculation results using the proposed model can accurately reflect the temperature change pattern of concrete in heat dissipation conditions.

scholarly journals The Extratropical Transition of Tropical Cyclones. Part I: Cyclone Evolution and Direct Impacts

2017 ◽  
Vol 145 (11) ◽  
pp. 4317-4344 ◽  
Author(s):  
Clark Evans ◽  
Kimberly M. Wood ◽  
Sim D. Aberson ◽  
Heather M. Archambault ◽  
Shawn M. Milrad ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Atlantic ◽  
North Pacific ◽  
Western North Pacific ◽  
North Atlantic Ocean ◽  
Wind Waves ◽  
Structural Evolution ◽  
Hurricane Sandy ◽  
Extratropical Transition ◽  
Hazard Communication

Extratropical transition (ET) is the process by which a tropical cyclone, upon encountering a baroclinic environment and reduced sea surface temperature at higher latitudes, transforms into an extratropical cyclone. This process is influenced by, and influences, phenomena from the tropics to the midlatitudes and from the meso- to the planetary scales to extents that vary between individual events. Motivated in part by recent high-impact and/or extensively observed events such as North Atlantic Hurricane Sandy in 2012 and western North Pacific Typhoon Sinlaku in 2008, this review details advances in understanding and predicting ET since the publication of an earlier review in 2003. Methods for diagnosing ET in reanalysis, observational, and model-forecast datasets are discussed. New climatologies for the eastern North Pacific and southwest Indian Oceans are presented alongside updates to western North Pacific and North Atlantic Ocean climatologies. Advances in understanding and, in some cases, modeling the direct impacts of ET-related wind, waves, and precipitation are noted. Improved understanding of structural evolution throughout the transformation stage of ET fostered in large part by novel aircraft observations collected in several recent ET events is highlighted. Predictive skill for operational and numerical model ET-related forecasts is discussed along with environmental factors influencing posttransition cyclone structure and evolution. Operational ET forecast and analysis practices and challenges are detailed. In particular, some challenges of effective hazard communication for the evolving threats posed by a tropical cyclone during and after transition are introduced. This review concludes with recommendations for future work to further improve understanding, forecasts, and hazard communication.

scholarly journals Elasto-plastic-adhesive DEM model for simulating hillslope debris flows: cross comparison with field experiments

2018 ◽  
Author(s):  
Adel Albaba ◽  
Massimiliano Schwarz ◽  
Corinna Wendeler ◽  
Bernard Loup ◽  
Luuk Dorren
Keyword(s):  
Numerical Model ◽  
Flow Velocity ◽  
Debris Flows ◽  
Field Experiments ◽  
Initial Conditions ◽  
Experimental Tests ◽  
Height Velocity ◽  
Model Parameters ◽  
Fine Content ◽  
Adhesive Model

Abstract. This paper presents a Discrete Element-based elasto-plastic-adhesive model which is adapted and tested for producing hillslope debris flows. The numerical model produces three phases of particle contacts: elastic, plastic and adhesion. The model capabilities of simulating different types of cohesive granular flows were tested with different ranges of flow velocities and heights. The basic model parameters, being the basal friction (ϕb) and normal restitution coefficient (ϵn), were calibrated using field experiments of hillslope debris flows impacting two sensors. Simulations of 50 m3 of material were carried out on a channelized surface that is 41 m long and 8 m wide. The calibration process was based on measurements of flow height, flow velocity and the pressure applied to a sensor. Results of the numerical model matched well those of the field data in terms of pressure and flow velocity while less agreement was observed for flow height. Those discrepancies in results were due in part to the deposition of material in the field test which are not reproducible in the model. A parametric study was conducted to further investigate that effect of model parameters and inclination angle on flow height, velocity and pressure. Results of best-fit model parameters against selected experimental tests suggested that a link might exist between the model parameters ϕb and ϵn and the initial conditions of the tested granular material (bulk density and water and fine contents). The good performance of the model against the full-scale field experiments encourages further investigation by conducting lab-scale experiments with detailed variation of water and fine content to better understand their link to the model's parameters.

scholarly journals Impact of Parameterization of Physical Processes on Simulation of Track and Intensity of Tropical Cyclone Nargis (2008) with WRF-NMM Model

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Sujata Pattanayak ◽  
U. C. Mohanty ◽  
Krishna K. Osuri
Keyword(s):  
Land Surface ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Surface Wind ◽  
India Meteorological Department ◽  
Heat Fluxes ◽  
Cumulus Convection ◽  
Cyclonic Storm ◽  
Parameterization Schemes ◽  
Cyclone Nargis

The present study is carried out to investigate the performance of different cumulus convection, planetary boundary layer, land surface processes, and microphysics parameterization schemes in the simulation of a very severe cyclonic storm (VSCS) Nargis (2008), developed in the central Bay of Bengal on 27 April 2008. For this purpose, the nonhydrostatic mesoscale model (NMM) dynamic core of weather research and forecasting (WRF) system is used. Model-simulated track positions and intensity in terms of minimum central mean sea level pressure (MSLP), maximum surface wind (10 m), and precipitation are verified with observations as provided by the India Meteorological Department (IMD) and Tropical Rainfall Measurement Mission (TRMM). The estimated optimum combination is reinvestigated with six different initial conditions of the same case to have better conclusion on the performance of WRF-NMM. A few more diagnostic fields like vertical velocity, vorticity, and heat fluxes are also evaluated. The results indicate that cumulus convection play an important role in the movement of the cyclone, and PBL has a crucial role in the intensification of the storm. The combination of Simplified Arakawa Schubert (SAS) convection, Yonsei University (YSU) PBL, NMM land surface, and Ferrier microphysics parameterization schemes in WRF-NMM give better track and intensity forecast with minimum vector displacement error.

scholarly journals Disease control as an optimization problem

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257958
Author(s):  
Miguel Navascués ◽  
Costantino Budroni ◽  
Yelena Guryanova
Keyword(s):  
Disease Control ◽  
Stochastic Models ◽  
Optimization Problem ◽  
Initial Conditions ◽  
Optimization Theory ◽  
Model Parameters ◽  
Grid Search ◽  
Brute Force ◽  
Time Required ◽  
Policy Optimization

In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set. This scheme runs the risk of leaving out more complex (and perhaps counter-intuitive) policies for disease control that could tackle the disease more efficiently. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease, and stochastic models. In addition, our methods allow for optimization over policies which remain constant over weekly periods, specified by either continuous or discrete (e.g.: lockdown on/off) government measures. We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020).

scholarly journals Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

2011 ◽  
Vol 11 (11) ◽  
pp. 30457-30485 ◽  
Author(s):  
P. Groenemeijer ◽  
G. C. Craig
Keyword(s):  
Large Scale ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Deep Convection ◽  
Horizontal Resolution ◽  
Ensemble Forecasting ◽  
Total Variance ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48 h forecasts at 7 km horizontal resolution were generated for a 2000 × 2000 km domain covering central Europe. Forecasts were made for seven case studies and characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

scholarly journals Calibration and validation of the Dynamic Wake Meandering model Part I: Bayesian estimation of model parameters using SpinnerLidar-derived wake characteristics

2021 ◽  
Author(s):  
Davide Conti ◽  
Nikolay Dimitrov ◽  
Alfredo Peña ◽  
Thomas Herges
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Flow Characteristics ◽  
Uncertainty Assessment ◽  
Wake Flow ◽  
Model Parameters ◽  
Wake Field ◽  
Wind Speeds ◽  
Standard Lead ◽  
Estimation Of Model Parameters

Abstract. In this first part of a two-part work, we study the calibration of the Dynamic Wake Meandering (DWM) model using high spatial and temporal resolution SpinnerLidar measurements of the wake field collected at the Scaled Wind Farm Technology (SWiFT) facility located in Lubbock, Texas, U.S.A. We derive two-dimensional wake flow characteristics including wake deficit, wake turbulence and wake meandering from the lidar observations under different atmospheric stability conditions, inflow wind speeds and downstream distances up to five rotor diameters. We then apply Bayesian inference to obtain a probabilistic calibration of the DWM model, where the resulting joint distribution of parameters allows both for model implementation and uncertainty assessment. We validate the resulting fully-resolved wake field predictions against the lidar measurements and discuss the most critical sources of uncertainty. The results indicate that the DWM model can accurately predict the mean wind velocity and turbulence fields in the far wake region beyond four rotor diameters, as long as properly-calibrated parameters are used and wake meandering time series are accurately replicated. We demonstrate that the current DWM-model parameters in the IEC standard lead to conservative wake deficit predictions. Finally, we provide practical recommendations for reliable calibration procedures.

scholarly journals Assessing the Potential of a Long-Term Climate Forecast for Cuba Using the WRF Model

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Lourdes Álvarez-Escudero ◽  
Yandy G. Mayor ◽  
Israel Borrajero-Montejo ◽  
Arnoldo Bezanilla-Morlot
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Weather Research And Forecasting ◽  
Tropical Storms ◽  
Precipitation Regime ◽  
Wide Debate ◽  
One Year ◽  
Cumulative Precipitation

Seasonal climatic prediction studies are a matter of wide debate all over the world. Cuba, a mainly agricultural nation, should greatly benefit from the knowledge, which is available months in advance of the precipitation regime and allows for the proper management of water resources. In this work, a series of six experiments were made with a mesoscale model WRF (Weather Research and Forecasting Model) that produced a 15-month forecast for each month of cumulative precipitation starting at two dates, and for three non-consecutive years with different meteorological characteristics: one dry year (2004), one year that started dry and turned rainy (2005), and one year where several tropical storms occurred (2008). ERA-Interim reanalysis data were used for the initial and border conditions and experiments started 1 month before the beginning of the rainy and the dry seasons, respectively. In a general sense, the experience of using WRF indicated that it was a valid resource for seasonal forecast, since the results obtained were in the same range as those reported by the literature for similar cases. Several limitations were revealed by the results: the forecasts underestimated the monthly cumulative precipitation figures, tropical storms entering through the borders sometimes followed courses different from the real courses inside the working domain, storms that developed inside the domain were not reproduced by WRF, and differences in initial conditions led to significantly different forecasts for the corresponding time steps (nonlinearity). Changing the model parameterizations and initial conditions of the ensemble forecast experiments was recommended.

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